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Record W7043591732

Taking Back Curriculum: Feminist Innovations towards Nonviolent Futures

2024· article· en· W7043591732 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumLiteracyFutures contractInformed consentMedia literacyCurriculum development
DOInot available

Abstract

fetched live from OpenAlex

This paper maps key factors that activate adult stakeholders in Ontario to support curriculum pertaining to consent and non-violence in K-12 education. The paper draws from a study that used three qualitative approaches: (1) the design of an original media literacy curriculum module for Ontario youth ages 13-15; (2) curriculum assessment of the module by diverse stakeholders in Ontario K-12 education (n=20); and (3) analysis of archival documents pertaining to consent education and media literacy in Ontario, including official curriculum and media reports. Four key factors united stakeholders in supporting K-12 curriculum pertaining to consent and non-violence in Ontario. Firstly, stakeholders are intrigued by media-based pedagogies that can facilitate consent education that is “culturally relevant” (Ladson-Billings 1994; 1995) for diverse learners in Ontario. Stakeholders are also more likely to support consent and violence prevention initiatives if accompanied by professional development and teaching tools. Educator collectives and political organizing also allow for more feminist and social-justice pedagogies in the classroom, including consent education. Finally, parent councils and community groups are essential places for activism and knowledge sharing that can meet the needs of community members, while addressing stakeholders’ attitudes and behaviours that gatekeep violence prevention initiatives in education.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0370.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.309
GPT teacher head0.604
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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